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It looks as if the hurricane will grow into a monster storm that will swallow every space from Washington to Toronto to Boston. But … that’s far from the correct interpretation. What this map actually shows is a range of possible paths that a storm (in this case, 2012’s Superstorm Sandy) might take over the next five days. The white shaded region projects one to three days out; the dotted region projects four to five days out. The black and white circles along the center line of each region indicate the most likely path for the eye of the storm, and the letters labeling the circles indicate whether the storm is a tropical storm (S) or a hurricane (H).

All this information combined makes up a “cone of uncertainty” and represents a 67% confidence level for where the storm might actually go. In other words, there’s only a 2 in 3 chance that it will actually end up within the boundaries drawn.

If you didn’t get that from the map, you are not alone. And that can be a serious problem if you’re trying to decide whether to evacuate a given area.

Uncertainty is a part of the weather forecasting business. Advancements in forecasting have allowed us to improve the accuracy of our models and reduce that uncertainty, which led forecasters to predict Sandy’s unusual path and intensification eight days ahead. Despite that progress, the forecasters still produce hard-to-interpret visualizations like the one seen here.

COMMON MISINTERPRETATIONS

In recent years, new efforts have emerged to create clearer alternatives.

There are five common misinterpretations of the “cone of uncertainty,” says Alberto Cairo, a data visualization expert who has been conducting research with his colleagues at the University of Miami to improve hurricane maps.

First, people assume that the cone delineates the area under threat, and that its boundaries indicate how big the storm will grow. Second, people rarely realize that the cone represents a 67% confidence interval—a detail disclosed within the map’s documentation rather than on the map itself.

Third, people often believe that the white and dotted regions signify something more than just a division between the days of the forecast. Some, for example, think the dots indicate the area that will be affected by heavy rain.

Fourth, people don’t understand the difference between watches and warnings, or whether one is more severe. And finally, people don’t know what the letters mean within the black and white circles—again, because an explanation doesn’t appear on the map itself.

Some of these misinterpretations can be fixed relatively easily, Cairo believes, such as by adding clearer labels and legends and using design techniques that follow standard visualization conventions. Others, he says, will be harder to crack. Humans tend to interpret graphics quite literally, rather than as representations of an abstract idea. Cairo, who is writing a visualization book with a chapter on uncertainty, says this challenge crops up constantly in science communication, not just in hurricane maps.

INFORMATION YOU NEED

Over the years, various researchers and media organizations have tested out new ways of visualizing the potential path of a hurricane. One method that has gained a lot of traction is to visualize each possible path as a separate line in what’s called a “spaghetti graph.”

Here’s an example of how the Washington Post implemented this technique for Hurricane Irma last year, with the less probable paths detailed in more faded hues:

Among the redesigns he’s seen, Cairo thinks this is one of the most promising. He intends to study it more with his colleagues as part of their research.

“We will try to test as many alternatives as possible to see which one or what combination of maps works better for people,” he says. Ultimately, the solution may not even involve showing the hurricane’s trajectory if their research finds that people don’t need that information to make a decision.

“Because that’s the whole purpose,” he says, “Giving people the information that they need to make a sound decision as to how they need to protect themselves and their families.”

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Karen Hao is the artificial intelligence reporter for MIT Technology Review. In particular she covers the ethics and social impact of the technology as well as its applications for social good. She also writes the AI newsletter, the Algorithm,… More which thoughtfully examines the field’s latest news and research. Prior to joining the publication, she was a reporter and data scientist at Quartz and an application engineer at the first startup to spin out of Google X.

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